"""Synthetic schedule, four-cell distribution and three-band photon accounting.
The schedule/spectrum describe the nominal input; spatial cells scale that input.
These are separate accounting examples, not a coupled crop or lighting model.
"""
import math
import time
from light_kernel import schedule, spatial, spectrum
DEFAULT=dict(ppfd=200,photoperiod=16,lights_on=6,distribution='uniform',area_weights='equal',spectral_mix='s1')
BOUNDS=dict(ppfd=(0,2000),photoperiod=(0,24),lights_on=(0,23.5))
ENUMS=dict(distribution=('uniform','uneven'),area_weights=('equal','last_triple'),spectral_mix=('s1','s2'))


def run(name,p):
    if name != 'light' or set(p)!=set(BOUNDS)|set(ENUMS):raise ValueError('Unknown experiment or parameters')
    for k,(lo,hi) in BOUNDS.items():
        if type(p[k]) not in (int,float) or not math.isfinite(p[k]) or not lo<=p[k]<=hi:raise ValueError('Parameter out of range: '+k)
    for k,values in ENUMS.items():
        if p[k] not in values:raise ValueError('Unknown option: '+k)
    for k in ('photoperiod','lights_on'):
        if abs(p[k]*2-round(p[k]*2))>1e-8:raise ValueError('Use half-hour lighting steps')
    start=time.perf_counter()
    cuts=sorted({0.,24.,p['lights_on'],(p['lights_on']+p['photoperiod'])%24})
    rows=[(a,b,p['ppfd'] if ((a+b)/2-p['lights_on'])%24<p['photoperiod'] else 0) for a,b in zip(cuts,cuts[1:])]
    temporal=schedule(rows)
    values=[p['ppfd']*v for v in ([1,1,1,1] if p['distribution']=='uniform' else [.5,1,1,1.5])]
    areas=[1,1,1,1] if p['area_weights']=='equal' else [1,1,1,3]
    space=spatial(values,areas,p['photoperiod'])
    bands=[p['ppfd']*v for v in ([.2,.3,.5] if p['spectral_mix']=='s1' else [.1,.2,.7])]
    spectral=spectrum(bands)
    def metric(k,v,u):
        result=dict(id=k,value=v,unit=u)
        if v is None:result['reason']='Undefined: denominator is zero'
        return result
    def series(k,x,y,xu,yu):return dict(id=k,x=x,y=y,x_unit=xu,y_unit=yu)
    xs=[];ys=[]
    for a,b,v in rows:xs.extend([a,b]);ys.extend([v,v])
    return dict(metrics=[metric('nominal_dli',temporal['dli_mol_m2_day'],'mol m⁻² day⁻¹'),metric('positive_light_hours',temporal['light_hours'],'h'),metric('dark_hours',temporal['dark_hours'],'h'),metric('area_mean_ppfd',space['mean_ppfd'],'μmol m⁻² s⁻¹'),metric('uniformity',space['min_over_mean'],'fraction'),metric('blue_fraction',spectral['fraction_400_500'],'fraction')],
        series=[series('cell_ppfd',[1,2,3,4],values,'cell','μmol m⁻² s⁻¹'),series('cell_area',[1,2,3,4],areas,'cell','relative area'),series('cell_dli',[1,2,3,4],space['cell_dli'],'cell','mol m⁻² day⁻¹'),series('spectral_bands',[450,550,650],bands,'nm band midpoint','μmol m⁻² s⁻¹'),series('nominal_schedule',xs,ys,'h','μmol m⁻² s⁻¹')],
        stdout='',provenance=dict(data_type='synthetic',compute_ms=(time.perf_counter()-start)*1000))


if __name__=='__main__':
    import argparse,json
    parser=argparse.ArgumentParser(description=__doc__);parser.add_argument('--parameters',default=json.dumps(DEFAULT))
    print(json.dumps(run('light',json.loads(parser.parse_args().parameters)),allow_nan=False))